The present invention relates to detecting airways in 3-dimensional medical images of the lungs, and more particularly to a system and method for detecting airways in computed tomography (CT) lung images using filters based on first and second order derivatives of the CT lung images.
Computed tomography (CT) is a medical imaging method whereby digital geometry processing is used to generate a three-dimensional image of the internal features of a patient from X-ray beams. Such CT imaging results in CT volume data which is a virtual representation of internal anatomical features of a patient. The CT volume data consists of multiple slices, or two-dimensional images, that can be combined to generate a three dimensional image, CT imaging is particularly useful because it can show several types of tissue including lung, bone, soft tissue and blood vessels, with great clarity. Accordingly, such imaging of the body can be used to diagnose problems such as cancers, cardiovascular disease, infectious disease, trauma and musculoskeletal disorders.
The respiratory system starts at the nose and mouth and continues through the airways to the lungs. The largest airway is the windpipe (trachea), which branches into two smaller airways: the left and right bronchi, which lead to the two lungs. The bronchi themselves divide many times before branching into smaller airways (bronchioles). These airways get progressively smaller as they branch out, until they are smaller than a millimeter in diameter. The airways appear as small tubular objects in CT data sets. Detection of the airways in CT image data is an important component in segmentation or quantitative analysis of the airways.
Conventional methods of determining the extent of the airways include region growing, morphological algorithms, and template matching. However, detection of the smaller airways using the conventional methods is difficult due to partial volume effects and noise. Three dimensional CT image data is made up of voxels. Each voxel has an intensity value corresponding to the density of the object. If a voxel is inside an airway, the voxel will tend to have a low intensity value representing the low-density air inside the airway. If a voxel is on an airway wall, the voxel will most likely have a higher intensity value than the inner portion of the airway. However, as the airways get smaller, single voxels can include an airway, an airway wall, and surrounding tissue. In this case, partial volume effects occur whereby the voxel combines the intensity values of the air, the airway wall, and the surrounding tissue. Thus, it is difficult to accurately detect airways and airway walls. Detection of smaller airways can also be difficult due to reconstruction artifacts and noise common in CT scanning.